EXPENSE REPORT GENERATION SYSTEM
Techniques for generating an expense report are disclosed. An expense report generation system monitors one or more data sources to obtain data corresponding to an employee's target activity. The expense report generation system compares the data corresponding to the employee's target activity with an expense trigger. The expense trigger includes one or more conditions that, when satisfied, identify an expense associated with the employee's target activity. The expense report generation system determines that the data corresponding to the employee's target activity satisfies the expense trigger. Responsive to determining that the data corresponding to the employee's target activity satisfies the expense trigger, the expense report generation system generates an expense description for the expense. The expense report generation system generates an expense report including the expense description.
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This application claims the benefit of U.S. Provisional Patent Application 62/898,695, filed Sep. 11, 2019, which is hereby incorporated by reference.
This application is related to: U.S. Provisional Patent Application 62/898,699, titled “Real-Time Expense Auditing System”; U.S. Provisional Patent Application 62/898,712, titled “Expense Report Reviewing Interface”; U.S. Provisional Patent Application 62/898,705, titled “Expense Receipt Processing System”; U.S. Provisional Patent Application 62/898,718, titled “Expense Report Submission Interface”; and U.S. Provisional Patent Application 62/898,724, titled “Reimbursable Expense Recommendation System”. All of the aforementioned patent applications are hereby incorporated by reference.
TECHNICAL FIELDThe present disclosure relates to machine learning techniques and applications. In particular, the present disclosure relates to training and using machine learning models to automate various aspects of expense reporting.
BACKGROUNDDuring the course of business-related activity (e.g., domestic or international travel, site visits and/or other kinds of business meetings, meals with customers and/or prospective customers, routine business operations, promotional events, and/or any other kind of business-related activity or combination thereof), employees sometimes incur expenses that are reimbursable by their employer as business expenses. To obtain reimbursement for such expenses, an employee typically submits an expense report. An expense report is a report that includes one or more expense descriptions. Each expense description includes expense data that describes one or more business expenses incurred by the employee. Expense data may include but is not limited to: a name of the employee that incurred the expense, a date the expense was incurred, a type of expense, a reason for the expense, an amount of the expense, a venue corresponding to the expense, a business project associated with the expense, and a number of employees that benefited from the expense. An expense description template or expense report template may define a set of mandatory and/or non-mandatory fields to be filled out when preparing an expense description or expense report.
Businesses typically impose limits on reimbursable business expenses. Each expense limit may apply to an entire organization, a particular business unit, and/or one or more particular employees. Expense auditing is the process of determining, for each expense described in an expense report, whether the expense is approved for reimbursement.
Generating and/or auditing expense reports may be subject to various kinds of errors and inefficiencies. If an employee neglects to include an incurred expense in an expense report, the employee may end up paying for that expense out-of-pocket. If an employee is not aware of an opportunity for reimbursement, the employee may fail to take advantage of that expense opportunity. If an employee fails to properly manage spending, the employee may incur expenses that are partially or wholly non-reimbursable. Because preparing expense reports manually is time-consuming, an employee may delay preparing an expense report and subsequently forget to include reimbursable expenses in the expense report. Some employees may habitually overspend relative to an expense limit, while other employees may habitually underspend relative to the same expense limit. Some employees may include non-reimbursable expenses in expense reports. Habitual overspending, underspending, and/or non-reimbursable expense reporting may adversely affect organizational and/or individual expense budgets. In addition, depending on the number of employees submitting expense reports and/or the complexity of the organization's expense reimbursement rules, auditing expense reports may be time-consuming and error-prone. Generating and/or auditing expense reports may be subject to many other kinds of errors and inefficiencies.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
The embodiments are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings. It should be noted that references to “an” or “one” embodiment in this disclosure are not necessarily to the same embodiment, and they mean at least one. In the drawings:
In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding. One or more embodiments may be practiced without these specific details. Features described in one embodiment may be combined with features described in a different embodiment. In some examples, well-known structures and devices are described with reference to a block diagram form in order to avoid unnecessarily obscuring the present invention.
1. GENERAL OVERVIEW
2. ARCHITECTURAL OVERVIEW
3. GENERATING AN EXPENSE REPORT
4. INTEGRATING MACHINE LEARNING INTO EXPENSE PROCESSING
5. ILLUSTRATIVE EXAMPLES
6. HARDWARE OVERVIEW
7. COMPUTER NETWORKS AND CLOUD NETWORKS
8. MICROSERVICE APPLICATIONS
9. MISCELLANEOUS; EXTENSIONS
1. General Overview
One or more embodiments improve the timeliness, thoroughness, and/or accuracy of expense reporting by identifying expenses that (a) may be reimbursable and (b) have not yet been submitted by an employee. An expense reporting system evaluates triggers, corresponding to sets of codified rules and/or automatically learned patterns, to determine whether an employee's activity is indicative of potentially reimbursable expenses. Embodiments may improve the timeliness of expense reporting by identifying potentially reimbursable expenses before an employee does. Embodiments may improve the thoroughness of expense reporting by proposing potentially reimbursable expenses that an employee may have otherwise overlooked. Embodiments may improve the accuracy of expense reporting by using available data to generate expense descriptions, rather than relying on an employee to manually enter all the data for the expense descriptions into spreadsheets or other electronic expense report templates.
The expense reporting system may further leverage machine learning to facilitate and automate various aspects of processing expense reports. In some embodiments, the expense reporting system learns how to classify and process expenses and activities based on a set of training examples. For example, the expense reporting system may automatically learn what patterns are predictive of the likelihood that an activity incurs a reimbursable expense even though the patterns are not hard-coded into the expense reporting system. When new activities or expenses are identified, the expense reporting system may estimate unknown labels or classifications based on the learned patterns. The expense reporting system may trigger one or more automated actions based on the estimated label for the new expenses or activities.
One or more embodiments described in this Specification and/or recited in the claims may not be included in this General Overview section.
2. Architectural Overview
In some embodiments, each of submitter interface 102, approver interface 106, auditor interface 108, and administrator interface 110 refers to hardware and/or software configured to facilitate communications between a user and an expense reporting service 112. A submitter interface 102 may be used by a user, such as an employee, who is responsible for preparing and submitting expense descriptions and/or expense reports. The submitter interface 102 may be associated with one or more devices for obtaining visual media that represents a receipt for an expense, such as a scanner 104, a camera, a video device, or any other kind of device configured to capture visual media. An approver interface 106 may be used by a user, such as an employee in a managerial role, who is responsible for approving expense reports prior to submission for reimbursement. In an embodiment, expense reports are not subject to managerial approval prior to submission for reimbursement. An auditor interface 108 may be used by a user, such as an employee in an auditor role, who is responsible for auditing expense reports. An administrator interface 110 may be used by a user, such as an employee in an administrative role, who is responsible for determining and/or configuring parameters, rules, etc., that are used by an expense reporting service 112. One or more of a submitter interface 102, approver interface 106, auditor interface 108, and administrator interface 110 may be the same interface. A user may have multiple roles corresponding to submitter, approver, auditor, and/or administrator. For example, an employee who audits expense reports may also submit their own expense reports.
In some embodiments, a user interface (e.g., submitter interface 102, approver interface 106, auditor interface 108, and/or administrator interface 110) renders user interface elements and receives input via user interface elements. Examples of interfaces include a graphical user interface (GUI), a command line interface (CLI), a haptic interface, and a voice command interface. Examples of user interface elements include checkboxes, radio buttons, dropdown lists, list boxes, buttons, toggles, text fields, date and time selectors, command lines, sliders, pages, and forms.
In some embodiments, different components of a user interface (e.g., submitter interface 102, approver interface 106, auditor interface 108, and/or administrator interface 110) are specified in different languages. The behavior of user interface elements is specified in a dynamic programming language, such as JavaScript. The content of user interface elements is specified in a markup language, such as hypertext markup language (HTML) or XML User Interface Language (XUL). The layout of user interface elements is specified in a style sheet language, such as Cascading Style Sheets (CSS). Alternatively, a user interface may be specified in one or more other languages, such as Java, C, or C++.
In some embodiments, an expense reporting service 112 includes an expense report generation engine 114. An expense report generation engine 114 refers to hardware and/or software configured to perform operations described herein (including such operations as may be incorporated by reference) for generating expense reports.
In some embodiments, an expense reporting service 112 includes an expense recommendation engine 116. An expense recommendation engine 116 refers to hardware and/or software configured to perform operations described herein (including such operations as may be incorporated by reference) for recommending expenses.
In some embodiments, an expense reporting service 112 includes an expense report auditing engine 118. An expense report auditing engine 118 refers to hardware and/or software configured to perform operations described herein (including such operations as may be incorporated by reference) for auditing expense descriptions and/or expense reports.
In some embodiments, an expense reporting service 112 includes a receipt processing engine 120. A receipt processing engine 120 refers to hardware and/or software configured to perform operations described herein (including such operations as may be incorporated by reference) for processing expense receipts.
In some embodiments, an expense reporting service 112 includes a user support engine 122. A user support engine 122 refers to hardware and/or software configured to perform operations described herein (including such operations as may be incorporated by reference) for processing and responding to user queries submitted to the expense reporting service 112.
In some embodiments, one or more components of the expense reporting service use a machine learning engine 124. Machine learning includes various techniques in the field of artificial intelligence that deal with computer-implemented, user-independent processes for solving problems that have variable inputs.
In some embodiments, the machine learning engine 124 trains a machine learning model 126 to perform one or more operations. Training a machine learning model 126 uses training data to generate a function that, given one or more inputs to the machine learning model 126, computes a corresponding output. The output may correspond to a prediction based on prior machine learning. In some embodiments, the output includes a label, classification, and/or categorization assigned to the provided input(s). The machine learning model 126 corresponds to a learned model for performing the desired operation(s) (e.g., labeling, classifying, and/or categorizing inputs). An expense reporting service 112 may use multiple machine learning engines 124 and/or multiple machine learning models 126 for different purposes.
In some embodiments, the machine learning engine 124 may use supervised learning, semi-supervised learning, unsupervised learning, reinforcement learning, and/or another training method or combination thereof. In supervised learning, labeled training data includes input/output pairs in which each input is labeled with a desired output (e.g., a label, classification, and/or categorization), also referred to as a supervisory signal. In semi-supervised learning, some inputs are associated with supervisory signals and other inputs are not associated with supervisory signals. In unsupervised learning, the training data does not include supervisory signals. Reinforcement learning uses a feedback system in which the machine learning engine 124 receives positive and/or negative reinforcement in the process of attempting to solve a particular problem (e.g., to optimize performance in a particular scenario, according to one or more predefined performance criteria). In some embodiments, the machine learning engine 124 initially uses supervised learning to train the machine learning model 126 and then uses unsupervised learning to update the machine learning model 126 on an ongoing basis.
In some embodiments, a machine learning engine 124 may use many different techniques to label, classify, and/or categorize inputs. A machine learning engine 124 may transform inputs into feature vectors that describe one or more properties (“features”) of the inputs. The machine learning engine 124 may label, classify, and/or categorize the inputs based on the feature vectors. Alternatively or additionally, a machine learning engine 124 may use clustering (also referred to as cluster analysis) to identify commonalities in the inputs. The machine learning engine 124 may group (i.e., cluster) the inputs based on those commonalities. The machine learning engine 124 may use hierarchical clustering, k-means clustering, and/or another clustering method or combination thereof. In some embodiments, a machine learning engine 124 includes an artificial neural network. An artificial neural network includes multiple nodes (also referred to as artificial neurons) and edges between nodes. Edges may be associated with corresponding weights that represent the strengths of connections between nodes, which the machine learning engine 124 adjusts as machine learning proceeds. Alternatively or additionally, a machine learning engine 124 may include a support vector machine. A support vector machine represents inputs as vectors. The machine learning engine 124 may label, classify, and/or categorizes inputs based on the vectors. The coordinates of the vectors and corresponding boundaries between different hyperplanes may be adjusted as machine learning proceeds. Alternatively or additionally, the machine learning engine 124 may use a naïve Bayes classifier to label, classify, and/or categorize inputs. Alternatively or additionally, given a particular input, a machine learning model may apply a decision tree to predict an output for the given input. Alternatively or additionally, a machine learning engine 124 may apply fuzzy logic in situations where labeling, classifying, and/or categorizing an input among a fixed set of mutually exclusive options is impossible or impractical. The aforementioned machine learning model 126 and techniques are discussed for exemplary purposes only and should not be construed as limiting one or more embodiments.
In some embodiments, as a machine learning engine 124 applies different inputs to a machine learning model 126, the corresponding outputs are not always accurate. As an example, the machine learning engine 124 may use supervised learning to train a machine learning model 126. After training the machine learning model 126, if a subsequent input is identical to an input that was included in labeled training data and the output is identical to the supervisory signal in the training data, then output is certain to be accurate. If an input is different from inputs that were included in labeled training data, then the machine learning engine 124 may generate a corresponding output that is inaccurate or of uncertain accuracy. In addition to producing a particular output for a given input, the machine learning engine 124 may be configured to produce an indicator representing a confidence (or lack thereof) in the accuracy of the output. A confidence indicator may include a numeric score, a Boolean value, and/or any other kind of indicator that corresponds to a confidence (or lack thereof) in the accuracy of the output.
In some embodiments, a data repository 128 is any type of storage unit and/or device (e.g., a file system, database, collection of tables, or any other storage mechanism) for storing data. Further, a data repository 128 may include multiple different storage units and/or devices. The multiple different storage units and/or devices may or may not be of the same type or located at the same physical site. Further, a data repository 128 may be implemented or may execute on the same computing system as one or more other components of the system 100. Alternatively or additionally, a data repository 128 may be implemented or executed on a computing system separate from one or more other components of the system 100. A data repository 128 may be communicatively coupled to one or more other components of the system 100 via a direct connection or via a network.
In some embodiments, a data repository 128 is configured to store historical expense data 130. Historical expense data 130 may include any kind of data that the expense reporting service 112 has previously received and/or generated in association with expenses. Specifically, the historical expense data 130 may include expense reports, expense descriptions, metadata associated with expenses (e.g., geotags, dates and times, explanatory notes, and/or another kind of metadata or combination thereof), and/or any other kind of data or combination thereof associated with expenses. Historical expense data 130 may include data that is associated with one or more employees' target activity, which may also be associated (directly or indirectly) with one or more expenses. For example, historical expense data 130 may include one or more itineraries, location check-ins, phone records, emails, social media messages, calendar appointments, and/or any other kind of data or combination thereof associated with target activity.
In some embodiments, a data repository 128 is configured to store one or more expense preferences 131. An expense preference 131 includes one or move values that indicates one or more employees' preferences related to expenses that the employee(s) may incur during target activity. For example, an expense preference 131 may indicate that an employee prefers ride sharing over public transportation. As another example, an expense preference 131 may indicate that an employee has a dietary restriction (e.g., vegetarian, vegan, kosher, etc.). As another example, an expense preference 131 may indicate that an employee likes or dislikes a particular restaurant, hotel, or other establishment. An embodiment, an expense reporting service 112 uses a machine learning engine 124 to infer one or more employee preferences 131 from historical expense data 130. One or more triggers described herein may be based, at least in part, on one or more expense preferences 131.
In some embodiments, a data repository 128 is configured to store one or more expense policies 132. An expense policy 132 may be a set of one or more codified rules corresponding to criteria for reimbursable expenses. For example, example, an expense policy 132 may define one or more expense categories that are used to categorize reimbursable expenses (e.g., meals, transportation, incidentals, equipment, etc.). As another example, an expense policy 132 may define an expense limit that is applicable to one or more employees and/or one or more expense categories for a particular unit of time (e.g., day, week, month, year, etc.). As another example, an expense policy 132 may identify one or more kinds of expenses and/or establishments (e.g., particular stores or restaurants) for which expenses are not reimbursable. Many different kinds of expense policy 132 may be defined. An expense policy 132 may apply the level of an entire organization, a business unit, a team, an individual, or any other set of one or more employees or combination thereof.
In some embodiments, a data repository 128 is configured to store one or more expense guidelines 134. An expense guideline 134 may be a set of one or more codified rules corresponding to best practices for expenses and/or responsible spending guidelines. An expense guideline 134 may be more restrictive than an expense policy 132. For example, a particular expense that satisfies an expense policy 132 may fail to satisfy an expense guideline 134 because, even though the expense is within an allowable limit under the expense policy 132, the expense is inconsistent with responsible spending guidelines. An expense guideline 134 may apply the level of an entire organization, a business unit, a team, an individual, or any other set of one or more employees or combination thereof.
In some embodiments, a data repository 128 is configured to store one or more expense patterns 136. An expense pattern 136 identifies a typical and/or expected arrangement of expenses associated with target activity. An expense pattern 136 may be associated with target activity having one or more shared characteristics (e.g., a certain kind of business trip, business-related activity for a particular category of employees, or any other kind of shared characteristic or combination thereof). An expense pattern 136 may identify expenses that are typical for target activity having the shared characteristic(s). In one example, an expense pattern 136 identifies that international business travel typically includes: (1) airfare to and from the destination; (2) a rental car, public transportation, and/or ride sharing at the destination; (3) a hotel for the duration of the trip; (4) an international data roaming plan; and (5) three meals per day at the destination. An expense reporting system 112 may use an expense pattern 136 to identify reimbursable expenses for which an employee may have neglected to submit an expense report (e.g., based on a gap or difference between reported expenses and the expense pattern 136), and/or recommended reimbursable expenses that an employee might otherwise overlook. In some embodiments, an expense reporting service 112 uses a machine learning engine 124 to infer one or more expense patterns 136, based at least in part on historical expense data 130.
In some embodiments, a data repository 128 is configured to store one or more expense triggers 138. An expense trigger 138 is a codified set of rules and/or a set of automatically learned patterns that captures one or more conditions for identifying expenses associated with one or more employees' target activity. An expense identified by an expense trigger may be an expense for which an employee has not yet prepared and/or submitted an expense report.
In some embodiments, an expense trigger 138 is based, at least in part, on data corresponding to target activity of an employee and/or historical expense data 130. As one example, an expense trigger 138 identifies that a transportation expense may be available when an employee travels from one location to another (e.g., from the employee's home or office to the airport). As another example, an expense trigger 138 identifies that a hotel expense may be available when geolocation data (e.g., from a global positioning system (GPS), a social media check-in, and/or any other kind of data source that supplies geolocation data) indicates that the user has arrived at a hotel or is leaving a hotel. As another example, an expense trigger 138 identifies that a meal expense may be available when geolocation data (e.g., from a global positioning system (GPS), a social media check-in, and/or any other kind of data source that supplies geolocation data) indicates that the user has visited a restaurant.
In some embodiments, when an expense trigger 138 identifies an expense for travel to a location, where return travel is also expected, the expense trigger 138 identifies an expense for the return travel. For example, if an employee prepares an expense description for a taxi to an airport, an expense trigger 138 may identify (e.g., based on an expense pattern 136 for international business travel), a corresponding expense for return travel from the airport.
In some embodiments, an expense trigger 138 is based, at least in part, on one or more expense descriptions prepared by one or more other employees who are traveling with the employee in question. In one example, three employees are participating in the same business trip and two of the employees prepare expense descriptions for a business meal at a particular restaurant. In this example, an expense trigger 138 may identify that a corresponding expense at the same restaurant may also apply to the third employee.
In some embodiments, an expense trigger 138 is based, at least in part, on one or more credit card statements for one or more employees. The expense trigger 138 may determine that a particular credit card charge is associated (e.g., corresponds in time and/or geographic location) with an employee's relevant activity. Based on the association between the credit card charge and the employee's targeted activity, the expense trigger 138 may identify the credit card charge as a potentially reimbursable expense.
In some embodiments, an expense trigger 138 is based, at least in part, on a typical and/or expected pairing between two or more different kinds of expenses. In one example, an employee purchases gas at a gas station. However, the employee has not entered an expense description corresponding to a car rental. Based on a typical and expected pairing between gasoline and car rental, an expense trigger 138 may identify a car rental as an available expense for the employee.
In some embodiments, an expense trigger 138 identifies similar expenses over time and identifies an opportunity to enter a recurring expense. As one example, an employee who travels frequently for business submits expense reports each month that include expense descriptions corresponding to an international data roaming plan. An expense trigger 138 may identify the international data roaming plan as a recurring expense. Based on identifying the international data roaming plan as a recurring expense, the expense reporting service 112 may present a message to the employee offering to make the charge a recurring expense, so that the employee does not need to enter the expense description each month.
Many different kinds of expense triggers 138 may be defined. In an embodiment, an expense reporting service 112 uses a machine learning engine 124 to determine an expense trigger 138 as part of a machine learning model 126. Machine learning engine 124 is able to automatically infer expense triggers even though the exact pattern may not have been seen before. Further, machine learning engine 124 is able to learn different patterns of behavior that qualify as an expense trigger 138 depending on context. For example, expense triggers may differ depending on employee attributes, such as employee title, clearance level, job responsibilities. Additionally or alternatively, expense triggers may vary between different groups of employees, such as between different companies or organizational departments within the same company. Additionally or alternatively, expense triggers may vary for different temporal patterns, and/or geographic patterns of incurred expenses.
In some embodiments, a data repository 128 is configured to store one or more expense recommendation triggers 139. An expense recommendation trigger 139 is a codified set of rules and/or a set of automatically learned patterns that capture one or more conditions for identifying recommended expenses that are known or expected to be reimbursable. A recommended expense may be an expense that the employee has not yet incurred. In some embodiments, an expense reporting service 112 uses a machine learning engine 124 to determine an expense recommendation trigger 139 as part of a machine learning model 126.
In some embodiments, an expense recommendation trigger 139 is based, at least in part, on data corresponding to target activity of an employee and/or historical expense data 130. For example, an expense recommendation trigger 139 may recommend less expensive spending options to an employee who has a tendency to spend above expense limits and/or above expense guidelines. As another example, an expense recommendation trigger 139 may recommend expenses that are popular among similarly situated employees, such as a particular restaurant that other employees have frequented and for which expenses tended to be reimbursed. As another example, an expense recommendation trigger 139 may recommend against frequenting a particular establishment for which expenses tended to be declined.
In some embodiments, an expense recommendation trigger 139 is based, at least in part, on one or more expense preferences 131. For example, an expense recommendation trigger 139 may identify a recommended restaurant for an employee who is vegan or who is meeting with a client who is vegan. As another example, an expense recommendation trigger 139 may identify a recommended restaurant or mode of transportation for an employee who prefers healthy options.
In some embodiments, expense recommendation trigger 139 is based, at least in part, on an expense policy 132 and/or an expense guideline 134. For example, an expense recommendation trigger 139 may identify recommended expenses that increase responsible spending behavior, for example by reducing spending, taking advantage of deals, earning rewards, etc.
In some embodiments, an expense recommendation trigger 139 is based, at least in part, on a determination that one expense is less expensive and/or more likely to be reimbursable than another expense. Recommending less expensive options may reduce expenses for an organization and decrease the incidence of expenses that need to be audited and/or are declined for reimbursement.
In some embodiments, an expense recommendation trigger 139 is based, at least in part, on an employee's spending score. An employee's spending score may be based, at least in part, on historical expense data 130 associated with the employee. For example, the employee spending score may be based on one or more of: whether the employee tends to be below spending limits; an average time that the employee takes to prepare expense descriptions for expenses that have already been incurred; an audit history of the employee (e.g., a history of allowed and/or rejected expense descriptions, which may be expressed as a ratio or some other metric); a comparison of the employee's past spending with a expense policy (e.g., a spending limit); and/or any other kind of data or combination thereof associated with the employee's spending. In some embodiments, employees with ‘better’ spending scores are at lower risk of audits than employees with ‘worse’ spending scores. An expense recommendation trigger 139 may identify less expensive options for employees with ‘worse’ spending scores than for employees with ‘better’ spending scores.
In some embodiments, an expense recommendation trigger 139 is based on one or more attributes of past, present, and/or planned target activity of an employee (e.g., a business trip or another kind of business-related activity). For example, trips of at least a threshold duration may qualify for certain reimbursable expenses (e.g., dry cleaning). As another example, flights of at least a threshold duration may qualify for a reimbursable seat upgrade. As another example, travel to international destinations may qualify for reimbursable international data roaming charges.
In some embodiments, an expense recommendation trigger 139 is based, at least in part, on an expense limit for a trip compared with an amount of expenses already incurred for the trip. For example, an expense recommendation trigger 139 may identify recommended expenses that are less expensive than other options, for an employee who is running out of expense budget on a trip. The expense recommendation trigger 139 may compare a remaining budget with a remaining time on the trip and recommend expenses that allocate the remaining budget across the remaining time.
In some embodiments, an expense recommendation trigger 139 is based, at least in part, on information about employees who are participating in the same target activities. For example, an expense recommendation trigger 139 may identify ride-sharing and/or other expense sharing opportunities for employees traveling to the same destination. The system 100 may present the recommended expense to one or more of those employees, to help encourage savings available by sharing expenses.
In some embodiments, a data repository 128 is configured to store one or more approval triggers 140. An approval trigger 140 is a codified set of rules and/or a set of automatically learned patterns that capture one or more conditions for requiring approval of an expense description and/or expense report before submitting the expense description and/or expense report for reimbursement. An approval trigger 140 may be based, at least in part, on data corresponding to target activity of an employee and/or historical expense data 130. For example, an approval trigger 140 may indicate that all expense description requires approval if the expense exceeds or is within a certain amount of an expense limit. As another example, an approval trigger 140 may indicate that all expense descriptions in a particular category, and/or all expense descriptions prepared for a particular employee, require approval. As another example, expense descriptions that violate an expense policy 132 and/or an expense guideline 134 may require approval. As another example, employees themselves may be required to approve expense descriptions that are generated by the expense reporting service 112 in a user-independent mode (e.g., based on an expense trigger 138). Many different kinds of approval triggers 140 may be defined. In some embodiments, an expense reporting service 112 uses a machine learning engine 124 to determine an approval trigger 140 as part of a machine learning model 126.
In some embodiments, a data repository 128 is configured to store one or more audit triggers 142. An audit trigger 142 is a codified set of rules and/or a set of automatically learned patterns that capture one or more conditions for requiring auditing of an expense report, and/or for determining that an expense report or description is at risk of being audited. An audit trigger 142 may be based, at least in part, on data corresponding to target activity of an employee and/or historical expense data 130. In some embodiments, an audit trigger 142 is based, at least in part, on an audit risk score associated with a particular expense description. An audit trigger 142 may be satisfied when an audit risk score satisfies one or more threshold criteria (e.g., the audit risk score may be above or below a threshold number, or any other kind of threshold criteria or combination thereof). In some embodiments, an expense reporting service 112 uses a machine learning engine 124 to determine an audit trigger 142 as part of a machine learning model 126.
In some embodiments, a data repository 128 is configured to store one or more user credentials 144. An expense reporting service 112 may use a user credential 144 to access an external data source 146 and obtain data from the external data source 146. A user credential 144 may include a username, user identifier (ID), password, private key, public key, and/or any other kind of credential or combination thereof. In some embodiments, an employee supplies a user credential 144 to an expense reporting system 122 via a graphical user interface. For example, the expense reporting service 112 may use three-party authentication to obtain a user credential 144 from an employee.
In some embodiments, user data that is input into machine learning engine 124 is anonymized. Personal identifying information (PII) and other sensitive information may be replaced with an anonymous identifier, such as a cryptographic hash of the user data. Machine learning engine 124 may use the anonymized data to learn patterns and make predictions for different employees, within the same or different organizations, having similar attributes without compromising or revealing sensitive employee data.
Information describing one or more components that are illustrated here within a data repository 128 may be implemented across any of components within the system 100. However, this information is illustrated within the data repository 128 for purposes of clarity and explanation.
In some embodiments, an expense reporting service 112 is configured to receive data from one or more external data sources 146. An external data source 146 refers to hardware and/or software operating independent of the expense reporting service 112, i.e., under control of a different entity (e.g., a different company or other kind of organization) than an entity that controls the expense reporting service 112. An external data source 146 may supply data associated with an employee's business-related activity, such as travel, dining, meals, itineraries, appointments, emails, phone data, social media messages, credit card statements (e.g., for a business-provided credit card), and/or any other kind of target activity or combination thereof. The data may include information associated with an employee's expenses, which may or may not be reimbursable.
Some examples of an external data source 146 supplying data to an expense reporting service 112 include, but are not limited to: an airline or travel agency supplying data associated with an itinerary and/or ticket purchase; a food ordering application supplying data associated with a food order; a ride sharing service (e.g., Uber™, Lyft™, or another ride sharing service) supplying data associated with an instance of ride sharing; and a social media application (e.g., Facebook™, Foursquare™, or another social media application) supplying data corresponding to a check-in at a location (e.g., a restaurant, hotel, entertainment venue, or other location). Many different kinds of external data sources 146 may supply many different kinds of data.
In some embodiments, an expense reporting service 112 is configured to retrieve data from an external data source 146 by ‘pulling’ the data via an application programming interface (API) of the external data source 146, using user credentials 144 that a user has provided for that particular external data source 146. Alternatively or additionally, an external data source 146 may be configured to ‘push’ data to the expense reporting service 112 via an API of the expense reporting service, using an access key, password, and/or other kind of credential that a user has supplied to the external data source 146. An expense reporting service 112 may be configured to receive data from an external data source 146 in many different ways.
In some embodiments, a reimbursement service 148 refers to hardware and/or software configured to perform operations for reimbursing approved expenses. For example, the reimbursement service 148 may be part of an accounting service that applies reimbursements for approved expenses to employee's paychecks and/or separate reimbursement checks, which may be mailed to employees and/or direct-deposited into employee's bank accounts. Many different techniques for reimbursing approved expenses exist.
In some embodiments, one or more components of the system 100 implemented on one or more digital devices. The term “digital device” generally refers to any hardware device that includes a processor. A digital device may refer to a physical device executing an application or a virtual machine. Examples of digital devices include a computer, a tablet, a laptop, a desktop, a netbook, a server, a web server, a network policy server, a proxy server, a generic machine, a function-specific hardware device, a hardware router, a hardware switch, a hardware firewall, a hardware firewall, a hardware network address translator (NAT), a hardware load balancer, a mainframe, a television, a content receiver, a set-top box, a printer, a mobile handset, a smartphone, a personal digital assistant (“PDA”), a wireless receiver and/or transmitter, a base station, a communication management device, a router, a switch, a controller, an access point, and/or a client device.
3. Generating an Expense Report
In some embodiments, a system (e.g., one or more components of system 100 illustrated in
In some embodiments, the system compares employee activity (specifically, data corresponding to one or more employees' target activity) with an expense trigger (Operation 204). As discussed above, an expense trigger is a codified set of rules and/or automatically learned patterns that capture one or more conditions for identifying expenses associated with one or more employees' business-related activity. An expense identified by an expense trigger may be an expense for which an employee has not yet prepared and/or submitted an expense report. An expense trigger may be based, at least in part, on data corresponding to business-related activity of an employee and/or historical expense data.
In some embodiments, based on the comparison, the system determines whether the expense trigger is satisfied (Operation 206). If the expense trigger is not satisfied, then the system continues monitoring employee activity (Operation 202). The system may continue monitoring employee activity, on an ongoing basis (e.g., as a background service), even if the expense trigger is satisfied.
In some embodiments, if the expense trigger is satisfied, the system determines whether additional data is needed (Operation 208). Specifically, the system may determine that not enough information is available to generate an expense description for the expense identified by the expense trigger. As one example, geolocation data and/or a calendar appointment may indicate that an employee has traveled to the airport in connection with a business trip. An expense trigger may identify an opportunity for the employee to expense the travel to the airport. However, the system may not have any information about the employee's mode of transportation to the airport. Many different kinds of expenses may require different kinds of additional data to generate a corresponding expense description.
In some embodiments, if additional data is needed, the system obtains additional data for the expense description (Operation 210). The system may present, in a graphical user interface, a message to the employee requesting the additional data. Responsive to the message, the employee may supply the requested data.
In some embodiments, the system generates an expense description (Operation 212). Specifically, the system generates an expense description corresponding to the expense identified by the expense trigger. The expense description includes one or more fields corresponding to information about the expense (e.g., date, time, location, amount, number of parties involved, expense category, an explanatory note, and/or any other kind of descriptive information or combination thereof). The system may generate the expense description responsive to user input corresponding to a user instruction to generate the expense description. Alternatively, the system may generate the expense description in an user-independent mode, i.e., without requesting or requiring any user input instructing the system to generate the expense description. User-independent generation of expense descriptions may help improve the timeliness and accuracy of expense reporting.
In some embodiments, the system automatically generates an expense description as a function of information received from one or more linked sources, such as cloud services, mobile applications, and mobile device sensors. One or more of the linked external sources may push data (e.g., via an API) to the expense reporting service 112 or the expense reporting service 112 may periodically pull the data as previously described. As an example, a linked ridesharing app may push information about the cost of a ride, the date and time of a ride, and geolocation data indicating the start and stop point of the ride. As another example, a linked mobile pay app may push data about the data and time of a purchases made via the app, a purchase amount, a geotag identifying the location of the purchase, and vendor information indicative of the category for the vendor/purchase. Responsive to receiving the data, the expense reporting service may automatically generate an expense description by formatting the data and arranging it in a particular order based on codified rules or patterns learned from historical expense data. In user-independent mode, the expense description may be automatically generated and added to an expense report file without any human intervention.
In some embodiments, the system determines whether user approval of the expense description is required (Operation 214). The system may determine whether approval is required based on one or more approval triggers. In some embodiments, if user approval of the expense description is required, the system requests user approval of the expense description (Operation 216). The system may present, in a graphical user interface, a message to an employee in an approval role requesting approval or rejection of the expense description. Responsive to the message, the employee in the approval role may supply input corresponding to approval or rejection of the expense description. In other embodiments, expenses may be automatically approved or rejected based on codified rules and/or learned patterns. For example, a machine learning model may determine that expenses following a certain pattern should be fully or partially rejected automatically without requiring any further review.
In some embodiments, the system determines whether the expense description is approved (Operation 218). If the expense description is not approved, then the system rejects the expense description (Operation 220). The system may present, in a graphical user interface, a message to an employee indicating that the expense description has been rejected. The message may indicate, for example, that the expense description will not be included in any expense report. If the message was rejected by a user other than the employee, the message may include information identifying the user who rejected the expense description.
In some embodiments, if the expense description is approved (Operation 218), or no user approval is required, the system generates an expense report (Operation 222). The system may generate the expense report responsive to user input corresponding to a user instruction to generate the expense report. Alternatively, the system may generate the expense report in an user-independent mode, i.e., without requesting or requiring any user input instructing the system to generate the expense report. User-independent generation of expense reports may help improve the timeliness and accuracy of expense reporting.
In some embodiments, the system submits the expense report (Operation 224). The system may submit the expense report responsive to user input corresponding to a user instruction to submit the expense report. Alternatively, the system may submit the expense report in an user-independent mode, i.e., without requesting or requiring any user input instructing the system to submit the expense report. User-independent submission of expense reports may help improve the timeliness and accuracy of expense reporting.
4. Integrating Machine Learning into Expense Processing
In some embodiments, the expense reporting service 112 leverages machine learning to facilitate and automate processes for handling expense data. Machine learning allows expense reporting service 112 to perform tasks and capture patterns that are not hard-coded or otherwise explicitly programmed into the system. Machine learning further allows expense reporting service 112 to adapt to different application use-cases and evolve over time without requiring complex reprograming or other changes in the underlying application code.
In some embodiments, a system (e.g., one or more components of system 100 illustrated in
In some embodiments, examples in the training set include multiple expenses and/or activities that are related. For instance, a single example may include a set of expenses and/or activities that were incurred by an employee on a single business trip. In this instance, the expenses and activities may be related (a) temporally since the expenses are likely to have occurred within a relatively short timeframe of the trip; (b) geographically since the trip was likely constrained to a limited number of locations; and (c) by entity since the expenses were incurred by the same employee.
In some embodiments, the system generates a set of feature vectors for the labeled examples (Operation 304). A feature vector for an example may be n-dimensional, where n represents the number of features in the vector. The number of features that are selected may vary depending on the particular implementation. The features may be curated in a supervised approach or automatically selected from extracted attributes during model training and/or tuning. Example features include information about the employee that incurred an expense (e.g., employee job title, clearance level, department), geographic information about where an expense or activity occurred (e.g., continent, country, state, city), temporal information about when an expense or activity occurred (e.g., date and time), categorical information about what type of an expense was incurred or activity performed (e.g., vendor identifier, vendor category, product identifier, product category, activity name, activity patterns), and the expense amount. In some embodiments, a feature within a feature vector is represented numerically by one or more bits. The system may convert categorical attributes to numerical representations using an encoding scheme, such as one hot encoding.
In some embodiments, the system generates one or more candidate machine learning models that apply weights as a function of extracted features (Operation 306). In some cases, the system may generate and train a candidate recurrent neural network model, such as a long short-term memory (LSTM) model. With recurrent neural networks, one or more network nodes or “cells” may include a memory. A memory allows individual nodes in the neural network to capture dependencies based on the order in which feature vectors are fed through the model. The weights applied to a feature vector representing one expense or activity may depend on its position within a sequence of feature vector representations. Thus, the nodes may have a memory to remember relevant temporal dependencies between different expenses and/or activities. For example, a dinner expense in isolation may have a first set of weights applied by nodes as a function of the respective feature vector for the expense. However, if the dinner expense is immediately preceded by an earlier dinner expense, then a different set of weights may be applied by one or more nodes based on the memory of the preceding expense. In this case, whether the second dinner expense is reimbursable or not may be affected by the first dinner expense. As another example, one or more nodes may apply different weights if an expense is unique or a duplicate of another expense on the same day. In this case, the trained machine learning model may automatically filter out and reject duplicate expenses made on the same day while recurring expenses (e.g., monthly subscriptions) may be permitted. Additionally or alternatively, the system may generate and train other candidate models, such as support vector machines, decision trees, Bayes classifiers, and/or fuzzy logic models, as previously described.
In some embodiments, the system compares the labels estimated through the one or more candidate models with observed labels to determine an estimation error (Operation 308). The system may perform this comparison for a test set of examples, which may be a subset of examples in the training dataset that were not used to generate and fit the candidate models. The total estimation error for a candidate may be computed as a function of the magnitude of the difference and/or the number of examples for which the estimated label was wrongly predicted.
In some embodiments, the system determines whether to adjust the weights and/or other model parameters based on the estimation error (Operation 310). Adjustments may be made until a candidate model that minimizes the estimation error or otherwise achieves a threshold level of estimation error is identified. The process may return to Operation 308 to make adjustments and continue training the machine learning model.
In some embodiments, the system selects a candidate machine learning model parameters based on the estimation error (Operation 312). For example, the system may select a machine learning model having weights and other model parameters (e.g., selected feature combinations used to form the feature vectors) that yield the lowest estimation error for the test dataset.
In some embodiments, the system trains a neural network using backpropagation. Backpropagation is a process of updating cell states in the neural network based on gradients determined as a function of the estimation error. With backpropagation, nodes are assigned a fraction of the estimated error based on the contribution to the output and adjusted based on the fraction. In recurrent neural networks, time is also factored into the backpropagation process. As previously mentioned, a given example may include a sequence of related expenses and/or activities incurred on a trip. Each expense or activity may be processed as a separate discrete instance of time. For instance, an example may include expenses e1, e2, and e3 corresponding to times t, t+1, and t+2, respectively. Backpropagation through time may perform adjustments through gradient descent starting at time t+2 and moving backward in time to t+1 and then to t. Further, the backpropagation process may adjust the memory parameters of a cell such that a cell remembers contributions from previous expenses in the sequence of expenses. For example, a cell computing a contribution for e3 may have a memory of the contribution of e2, which has a memory of e1. The memory may serve as a feedback connection such that the output of a cell at one time (e.g., t) is used as an input to the next time in the sequence (e.g., t+1). The gradient descent techniques may account for these feedback connections such that the contribution of one expense or activity to a cell's output may affect the contribution of the next expense or activity in the cell's output. Thus, the contribution of e1 may affect the contribution of e2, etc.
Additionally or alternatively, the system may train other types of machine learning models. For example, the system may adjust the boundaries of a hyperplane in a support vector machine or node weights within a decision tree model to minimize estimation error. Once trained, the machine learning model may be used to estimate labels for new examples of expenses.
In some embodiments, the system (e.g., one or more components of system 100 illustrated in
In some embodiments, the system generates a set of one or more feature vectors based on the set of one or more expenses or activities (Operation 404). The system may form the feature vectors in the same manner used to train the machine learning model. The feature vectors may be a unique example such that the combination of feature values and/or sequence of feature vectors was not included in the training dataset.
In some embodiments, the system inputs the set of one or more feature vectors to the trained machine learning model to estimate a label for the new set of one or more expenses and/or activities (Operation 406). In the case of a recurrent neural network, for example, the system may perform forward propagation using a sequence of feature vectors representing different expenses and/or activities in the order that the expenses and/or activities occurred. As another example, in the case of a support vector machine, the system may compute a location in the hyperplane for the feature vector relative to the hyperplane boundaries. As another example, the system may follow a decision tree as a function of the input set of one or more feature vectors.
In some embodiments, the estimated label corresponds to a classification for an expense or activity that is output by the machine learning model as a function of the input feature vector and the patterns learned from the training dataset. For example, the trained machine learning model may classify an expense as “reimbursable” or “non-reimbursable”. As another example, the trained machine learning model may classify an activity as an expense trigger or not an expense trigger. Additionally or alternatively, the trained machine learning model may map an activity or expense to a category, such as travel, dining, continuing learning education, office supplies, software licenses, promotional material, etc. Additionally or alternatively, the trained machine learning model may output other classifications depending on the labels that are input.
In some embodiments, a label includes a numerical value. For example, a machine learning model may be trained to estimate a percentage or amount of an expense that is reimbursable for a given expense. The corresponding feature vector may be fed as input to the trained model, which may output an estimated percentage or amount based on patterns learned from the training dataset.
In some embodiments, the system triggers one or more automated actions based on the estimated label (Operation 408). Example actions may include initiating operations described herein for processing an expense trigger, automatically adding an expense to an electronic expense report, generating an expense description, prompting a user for an expense description, determining a file storage location for an expense record, rejecting an expense, routing an expense to an approver, sending notification messages, and/or generating and presenting GUI objects.
In some embodiments, the system may determine what action to trigger based in part on the uncertainty of an estimation. For example, the system may monitor a set of expenses provided by a linked mobile pay app during a trip. The system may use the trained machine learning model to predict that a set of expenses is reimbursable within a certain confidence level. If the confidence level exceeds a threshold, then the expenses may be automatically added to one or more expense reports without requiring any user input. If the confidence level is below the threshold, then the system may query the user incurring the expenses in real-time (e.g., via a mobile app or other interface) whether the expense should be added to an expense report. In other cases, the expenses may be routed to another approver for further review before they are added.
In some embodiments, the system determines a storage location for expenses based on the estimated labels. As an example, the system may generate a set of files, tables, or other data objects. Different data objects may group expenses that are associated with a particular category of expense. For instance, a first data object may store expenses categorized as “software licenses”, a second data object may store expenses categorized as “dining”, etc. As new expenses are received, the system may predict how to categorize the expense using the trained machine learning model and add the expense to the corresponding data object.
5. Illustrative Examples
A detailed example is described below for purposes of clarity. Components and/or operations described below should be understood as one specific example which may not be applicable to certain embodiments. Accordingly, components and/or operations described below should not be construed as limiting the scope of any of the claims.
6. Hardware Overview
According to one embodiment, the techniques described herein are implemented by one or more special-purpose computing devices (i.e., computing devices specially configured to perform certain functionality). The special-purpose computing devices may be hard-wired to perform the techniques, or may include digital electronic devices such as one or more application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or network processing units (NPUs) that are persistently programmed to perform the techniques, or may include one or more general purpose hardware processors programmed to perform the techniques pursuant to program instructions in firmware, memory, other storage, or a combination. Such special-purpose computing devices may also combine custom hard-wired logic, ASICs, FPGAs, or NPUs with custom programming to accomplish the techniques. The special-purpose computing devices may be desktop computer systems, portable computer systems, handheld devices, networking devices or any other device that incorporates hard-wired and/or program logic to implement the techniques.
For example,
Computer system 600 also includes a main memory 606, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 602 for storing information and instructions to be executed by processor 604. Main memory 606 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by processor 604. Such instructions, when stored in non-transitory storage media accessible to processor 604, render computer system 600 into a special-purpose machine that is customized to perform the operations specified in the instructions.
Computer system 600 further includes a read only memory (ROM) 608 or other static storage device coupled to bus 602 for storing static information and instructions for processor 604. A storage device 610, such as a magnetic disk or optical disk, is provided and coupled to bus 602 for storing information and instructions.
Computer system 600 may be coupled via bus 602 to a display 612, such as a liquid crystal display (LCD), plasma display, electronic ink display, cathode ray tube (CRT) monitor, or any other kind of device for displaying information to a computer user. An input device 614, including alphanumeric and other keys, may be coupled to bus 602 for communicating information and command selections to processor 604. Alternatively or in addition, the computer system 600 may receive user input via a cursor control 616, such as a mouse, a trackball, a trackpad, a touchscreen, or cursor direction keys for communicating direction information and command selections to processor 604 and for controlling cursor movement on display 612. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), that allows the device to specify positions in a plane. The display 612 may be configured to receive user input via one or more pressure-sensitive sensors, multi-touch sensors, and/or gesture sensors. Alternatively or in addition, the computer system 600 may receive user input via a microphone, video camera, and/or some other kind of user input device (not shown).
Computer system 600 may implement the techniques described herein using customized hard-wired logic, one or more ASICs or FPGAs, firmware and/or program logic which in combination with the computer system causes or programs computer system 600 to be a special-purpose machine. According to one embodiment, the techniques herein are performed by computer system 600 in response to processor 604 executing one or more sequences of one or more instructions contained in main memory 606. Such instructions may be read into main memory 606 from another storage medium, such as storage device 610. Execution of the sequences of instructions contained in main memory 606 causes processor 604 to perform the process steps described herein. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions.
The term “storage media” as used herein refers to any non-transitory media that store data and/or instructions that cause a machine to operate in a specific fashion. Such storage media may comprise non-volatile media and/or volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 610. Volatile media includes dynamic memory, such as main memory 606. Common forms of storage media include, for example, a floppy disk, a flexible disk, hard disk, solid state drive, magnetic tape, or any other magnetic data storage medium, a CD-ROM, any other optical data storage medium, any physical medium with patterns of holes, a RAM, a programmable read-only memory (PROM), and erasable PROM (EPROM), a FLASH-EPROM, non-volatile random-access memory (NVRAM), any other memory chip or cartridge, content-addressable memory (CAM), and ternary content-addressable memory (TCAM).
Storage media is distinct from but may be used in conjunction with transmission media. Transmission media participates in transferring information between storage media. For example, transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise bus 602. Transmission media can also take the form of acoustic or light waves, such as those generated during radio-wave and infra-red data communications.
Various forms of media may be involved in carrying one or more sequences of one or more instructions to processor 604 for execution. For example, the instructions may initially be carried on a magnetic disk or solid state drive of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a network, via a network interface controller (NIC), such as an Ethernet controller or Wi-Fi controller. A NIC local to computer system 600 can receive the data from the network and place the data on bus 602. Bus 602 carries the data to main memory 606, from which processor 604 retrieves and executes the instructions. The instructions received by main memory 606 may optionally be stored on storage device 610 either before or after execution by processor 604.
Computer system 600 also includes a communication interface 618 coupled to bus 602. Communication interface 618 provides a two-way data communication coupling to a network link 620 that is connected to a local network 622. For example, communication interface 618 may be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, communication interface 618 may be a local area network 30 (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, communication interface 618 sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.
Network link 620 typically provides data communication through one or more networks to other data devices. For example, network link 620 may provide a connection through local network 622 to a host computer 624 or to data equipment operated by an Internet Service Provider (ISP) 626. ISP 626 in turn provides data communication services through the world wide packet data communication network now commonly referred to as the “Internet” 628. Local network 622 and Internet 628 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on network link 620 and through communication interface 618, which carry the digital data to and from computer system 600, are example forms of transmission media.
Computer system 600 can send messages and receive data, including program code, through the network(s), network link 620 and communication interface 618. In the Internet example, a server 630 might transmit a requested code for an application program through Internet 628, ISP 626, local network 622 and communication interface 618.
The received code may be executed by processor 604 as it is received, and/or stored in storage device 610, or other non-volatile storage for later execution.
7. Computer Networks and Cloud Networks
In one or more embodiments, a computer network provides connectivity among a set of nodes running software that utilizes techniques as described herein. The nodes may be local to and/or remote from each other. The nodes are connected by a set of links. Examples of links include a coaxial cable, an unshielded twisted cable, a copper cable, an optical fiber, and a virtual link.
A subset of nodes implements the computer network. Examples of such nodes include a switch, a router, a firewall, and a network address translator (NAT). Another subset of nodes uses the computer network. Such nodes (also referred to as “hosts”) may execute a client process and/or a server process. A client process makes a request for a computing service (such as, execution of a particular application, and/or storage of a particular amount of data). A server process responds by executing the requested service and/or returning corresponding data.
A computer network may be a physical network, including physical nodes connected by physical links. A physical node is any digital device. A physical node may be a function-specific hardware device, such as a hardware switch, a hardware router, a hardware firewall, and a hardware NAT. Additionally or alternatively, a physical node may be any physical resource that provides compute power to perform a task, such as one that is configured to execute various virtual machines and/or applications performing respective functions. A physical link is a physical medium connecting two or more physical nodes. Examples of links include a coaxial cable, an unshielded twisted cable, a copper cable, and an optical fiber.
A computer network may be an overlay network. An overlay network is a logical network implemented on top of another network (such as, a physical network). Each node in an overlay network corresponds to a respective node in the underlying network. Hence, each node in an overlay network is associated with both an overlay address (to address to the overlay node) and an underlay address (to address the underlay node that implements the overlay node). An overlay node may be a digital device and/or a software process (such as, a virtual machine, an application instance, or a thread) A link that connects overlay nodes is implemented as a tunnel through the underlying network. The overlay nodes at either end of the tunnel treat the underlying multi-hop path between them as a single logical link. Tunneling is performed through encapsulation and decapsulation.
In an embodiment, a client may be local to and/or remote from a computer network. The client may access the computer network over other computer networks, such as a private network or the Internet. The client may communicate requests to the computer network using a communications protocol, such as Hypertext Transfer Protocol (HTTP). The requests are communicated through an interface, such as a client interface (such as a web browser), a program interface, or an application programming interface (API).
In an embodiment, a computer network provides connectivity between clients and network resources. Network resources include hardware and/or software configured to execute server processes. Examples of network resources include a processor, a data storage, a virtual machine, a container, and/or a software application. Network resources are shared amongst multiple clients. Clients request computing services from a computer network independently of each other. Network resources are dynamically assigned to the requests and/or clients on an on-demand basis. Network resources assigned to each request and/or client may be scaled up or down based on, for example, (a) the computing services requested by a particular client, (b) the aggregated computing services requested by a particular tenant, and/or (c) the aggregated computing services requested of the computer network. Such a computer network may be referred to as a “cloud network.”
In an embodiment, a service provider provides a cloud network to one or more end users. Various service models may be implemented by the cloud network, including but not limited to Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS). In SaaS, a service provider provides end users the capability to use the service provider's applications, which are executing on the network resources. In PaaS, the service provider provides end users the capability to deploy custom applications onto the network resources. The custom applications may be created using programming languages, libraries, services, and tools supported by the service provider. In IaaS, the service provider provides end users the capability to provision processing, storage, networks, and other fundamental computing resources provided by the network resources. Any applications, including an operating system, may be deployed on the network resources.
In an embodiment, various deployment models may be implemented by a computer network, including but not limited to a private cloud, a public cloud, and a hybrid cloud. In a private cloud, network resources are provisioned for exclusive use by a particular group of one or more entities (the term “entity” as used herein refers to a corporation, organization, person, or other entity). The network resources may be local to and/or remote from the premises of the particular group of entities. In a public cloud, cloud resources are provisioned for multiple entities that are independent from each other (also referred to as “tenants” or “customers”). The computer network and the network resources thereof are accessed by clients corresponding to different tenants. Such a computer network may be referred to as a “multi-tenant computer network.” Several tenants may use a same particular network resource at different times and/or at the same time. The network resources may be local to and/or remote from the premises of the tenants. In a hybrid cloud, a computer network comprises a private cloud and a public cloud. An interface between the private cloud and the public cloud allows for data and application portability. Data stored at the private cloud and data stored at the public cloud may be exchanged through the interface. Applications implemented at the private cloud and applications implemented at the public cloud may have dependencies on each other. A call from an application at the private cloud to an application at the public cloud (and vice versa) may be executed through the interface.
In an embodiment, tenants of a multi-tenant computer network are independent of each other. For example, one tenant (through operation, tenant-specific practices, employees, and/or identification to the external world) may be separate from another tenant. Different tenants may demand different network requirements for the computer network. Examples of network requirements include processing speed, amount of data storage, security requirements, performance requirements, throughput requirements, latency requirements, resiliency requirements, Quality of Service (QoS) requirements, tenant isolation, and/or consistency. The same computer network may need to implement different network requirements demanded by different tenants.
In one or more embodiments, in a multi-tenant computer network, tenant isolation is implemented to ensure that the applications and/or data of different tenants are not shared with each other. Various tenant isolation approaches may be used.
In an embodiment, each tenant is associated with a tenant ID. Each network resource of the multi-tenant computer network is tagged with a tenant ID. A tenant is permitted access to a particular network resource only if the tenant and the particular network resources are associated with a same tenant ID.
In an embodiment, each tenant is associated with a tenant ID. Each application, implemented by the computer network, is tagged with a tenant ID. Additionally or alternatively, each data structure and/or dataset, stored by the computer network, is tagged with a tenant ID. A tenant is permitted access to a particular application, data structure, and/or dataset only if the tenant and the particular application, data structure, and/or dataset are associated with a same tenant ID.
As an example, each database implemented by a multi-tenant computer network may be tagged with a tenant ID. Only a tenant associated with the corresponding tenant ID may access data of a particular database. As another example, each entry in a database implemented by a multi-tenant computer network may be tagged with a tenant ID. Only a tenant associated with the corresponding tenant ID may access data of a particular entry. However, the database may be shared by multiple tenants.
In an embodiment, a subscription list indicates which tenants have authorization to access which applications. For each application, a list of tenant IDs of tenants authorized to access the application is stored. A tenant is permitted access to a particular application only if the tenant ID of the tenant is included in the subscription list corresponding to the particular application.
In an embodiment, network resources (such as digital devices, virtual machines, application instances, and threads) corresponding to different tenants are isolated to tenant-specific overlay networks maintained by the multi-tenant computer network. As an example, packets from any source device in a tenant overlay network may only be transmitted to other devices within the same tenant overlay network. Encapsulation tunnels are used to prohibit any transmissions from a source device on a tenant overlay network to devices in other tenant overlay networks. Specifically, the packets, received from the source device, are encapsulated within an outer packet. The outer packet is transmitted from a first encapsulation tunnel endpoint (in communication with the source device in the tenant overlay network) to a second encapsulation tunnel endpoint (in communication with the destination device in the tenant overlay network). The second encapsulation tunnel endpoint decapsulates the outer packet to obtain the original packet transmitted by the source device. The original packet is transmitted from the second encapsulation tunnel endpoint to the destination device in the same particular overlay network.
8. Microservice Applications
According to one or more embodiments, the techniques described herein are implemented in a microservice architecture. A microservice in this context refers to software logic designed to be independently deployable, having endpoints that may be logically coupled to other microservices to build a variety of applications. Applications built using microservices are distinct from monolithic applications, which are designed as a single fixed unit and generally comprise a single logical executable. With microservice applications, different microservices are independently deployable as separate executables. Microservices may communicate using Hypertext Transfer Protocol (HTTP) messages and/or according to other communication protocols via API endpoints. Microservices may be managed and updated separately, written in different languages, and be executed independently from other microservices.
Microservices provide flexibility in managing and building applications. Different applications may be built by connecting different sets of microservices without changing the source code of the microservices. Thus, the microservices act as logical building blocks that may be arranged in a variety of ways to build different applications. Microservices may provide monitoring services that notify a microservices manager (such as If-This-Then-That (IFTTT), Zapier, or Oracle Self-Service Automation (OSSA)) when trigger events from a set of trigger events exposed to the microservices manager occur. Microservices exposed for an application may alternatively or additionally provide action services that perform an action in the application (controllable and configurable via the microservices manager by passing in values, connecting the actions to other triggers and/or data passed along from other actions in the microservices manager) based on data received from the microservices manager. The microservice triggers and/or actions may be chained together to form recipes of actions that occur in optionally different applications that are otherwise unaware of or have no control or dependency on each other. These managed applications may be authenticated or plugged in to the microservices manager, for example, with user-supplied application credentials to the manager, without requiring reauthentication each time the managed application is used alone or in combination with other applications.
In one or more embodiments, microservices may be connected via a GUI. For example, microservices may be displayed as logical blocks within a window, frame, other element of a GUI. A user may drag and drop microservices into an area of the GUI used to build an application. The user may connect the output of one microservice into the input of another microservice using directed arrows or any other GUI element. The application builder may run verification tests to confirm that the output and inputs are compatible (e.g., by checking the datatypes, size restrictions, etc.)
Triggers
The techniques described above may be encapsulated into a microservice, according to one or more embodiments. In other words, a microservice may trigger a notification (into the microservices manager for optional use by other plugged in applications, herein referred to as the “target” microservice) based on the above techniques and/or may be represented as a GUI block and connected to one or more other microservices. The trigger condition may include absolute or relative thresholds for values, and/or absolute or relative thresholds for the amount or duration of data to analyze, such that the trigger to the microservices manager occurs whenever a plugged-in microservice application detects that a threshold is crossed. For example, a user may request a trigger into the microservices manager when the microservice application detects a value has crossed a triggering threshold.
In one embodiment, the trigger, when satisfied, might output data for consumption by the target microservice. In another embodiment, the trigger, when satisfied, outputs a binary value indicating the trigger has been satisfied, or outputs the name of the field or other context information for which the trigger condition was satisfied. Additionally or alternatively, the target microservice may be connected to one or more other microservices such that an alert is input to the other microservices. Other microservices may perform responsive actions based on the above techniques, including, but not limited to, deploying additional resources, adjusting system configurations, and/or generating GUIs.
Actions
In one or more embodiments, a plugged-in microservice application may expose actions to the microservices manager. The exposed actions may receive, as input, data or an identification of a data object or location of data, that causes data to be moved into a data cloud.
In one or more embodiments, the exposed actions may receive, as input, a request to increase or decrease existing alert thresholds. The input might identify existing in-application alert thresholds and whether to increase or decrease, or delete the threshold. Additionally or alternatively, the input might request the microservice application to create new in-application alert thresholds. The in-application alerts may trigger alerts to the user while logged into the application, or may trigger alerts to the user using default or user-selected alert mechanisms available within the microservice application itself, rather than through other applications plugged into the microservices manager.
In one or more embodiments, the microservice application may generate and provide an output based on input that identifies, locates, or provides historical data, and defines the extent or scope of the requested output. The action, when triggered, causes the microservice application to provide, store, or display the output, for example, as a data model or as aggregate data that describes a data model.
9. Miscellaneous; Extensions
Embodiments are directed to a system with one or more devices that include a hardware processor and that are configured to perform any of the operations described herein and/or recited in any of the claims below.
In an embodiment, a non-transitory computer readable storage medium comprises instructions which, when executed by one or more hardware processors, causes performance of any of the operations described herein and/or recited in any of the claims.
Any combination of the features and functionalities described herein may be used in accordance with one or more embodiments. In the foregoing specification, embodiments have been described with reference to numerous specific details that may vary from implementation to implementation. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. The sole and exclusive indicator of the scope of the invention, and what is intended by the applicants to be the scope of the invention, is the literal and equivalent scope of the set of claims that issue from this application, in the specific form in which such claims issue, including any subsequent correction.
Claims
1. One or more non-transitory machine-readable media storing instructions which, when executed by one or more processors, cause:
- monitoring, by an expense report generation system, one or more data sources to obtain data corresponding to target activity of an employee;
- comparing, by the expense report generation system, the data corresponding to target activity of the employee with an expense trigger comprising one or more conditions that, when satisfied, identifies a first set of one or more expenses associated with the target activity of the employee;
- determining, by the expense report generation system, that the data corresponding to target activity of the employee satisfies the expense trigger;
- responsive to determining that the data corresponding to target activity of the employee satisfies the expense trigger: generating, by the expense report generation system, a first expense description for the first set of one or more expenses;
- generating, by the expense report generation system, an expense report comprising the first expense description.
2. The one or more media of claim 1, wherein the employee did not provide any expense description associated with the first set of one or more expenses prior to the expense report generation system generating the first expense description.
3. The one or more media of claim 1, wherein comparing the data corresponding to target activity of the employee with the expense trigger comprises:
- applying the data corresponding to target activity of the employee to a machine learning model.
4. The one or more media of claim 1, wherein generating the expense report is performed without user approval of the first expense description.
5. The one or more media of claim 1, further storing instructions which, when executed by one or more processors, cause:
- submitting the expense report for reimbursement without user approval of the expense report.
6. The one or more media of claim 1, wherein the one or more conditions comprise detecting movement of the employee from a first location to a second location.
7. The one or more media of claim 6, further storing instructions which, when executed by one or more processors, cause:
- responsive to detecting movement of the employee from the first location to the second location:
- generating a second expense description associated with movement of the employee from the second location to the first location.
8. The one or more media of claim 1, wherein the one or more conditions comprise detecting that the employee has visited a particular location.
9. The one or more media of claim 1, wherein the one or more conditions comprise detecting an expense description submission by a co-traveler of the employee.
10. The one or more media of claim 1, wherein the one or more conditions comprise detecting a credit card charge that is associated with target activity of the employee.
11. The one or more media of claim 10, wherein detecting the credit card charge that is associated with the target activity of the employee comprises determining that the credit card charge correlates temporally with the target activity of the employee.
12. The one or more media of claim 10, wherein detecting the credit card charge that is associated with the target activity of the employee comprises determining that the credit card charge correlates geographically with the target activity of the employee.
13. The one or more media of claim 1, wherein the one or more data sources comprise one or more applications operating independent of the expense report generation system.
14. The one or more media of claim 13, wherein the one or more applications operating independent of the expense report generation system comprise one or more of a ride sharing application or a food ordering application.
15. The one or more media of claim 1, wherein the expense trigger is associated with an expense template that codifies one or more patterns associated with target activity having one or more shared characteristics.
16. The one or more media of claim 15, wherein determining that the data corresponding to target activity of the employee satisfies the expense trigger comprises determining that the employee has not submitted an expected expense according to the expense template.
17. The one or more media of claim 1, wherein determining that the data corresponding to target activity of the employee satisfies the expense trigger comprises detecting that the user has submitted a second expense item that is typically associated with the first expense item.
18. The one or more media of claim 1:
- wherein the first expense description is associated with a recurring expense;
- wherein determining that the data corresponding to target activity of the employee satisfies the expense trigger comprises detecting that the employee qualifies for the recurring expense.
19. A system comprising:
- at least one device including a hardware processor;
- the system being configured to perform operations comprising:
- monitoring, by an expense report generation system, one or more data sources to obtain data corresponding to target activity of an employee;
- comparing, by the expense report generation system, the data corresponding to target activity of the employee with an expense trigger comprising one or more conditions that, when satisfied, identifies a first set of one or more expenses associated with the target activity of the employee;
- determining, by the expense report generation system, that the data corresponding to target activity of the employee satisfies the expense trigger;
- responsive to determining that the data corresponding to target activity of the employee satisfies the expense trigger: generating, by the expense report generation system, a first expense description for the first set of one or more expenses;
- generating, by the expense report generation system, an expense report comprising the first expense description.
20. A method comprising:
- monitoring, by an expense report generation system, one or more data sources to obtain data corresponding to target activity of an employee;
- comparing, by the expense report generation system, the data corresponding to target activity of the employee with an expense trigger comprising one or more conditions that, when satisfied, identifies a first set of one or more expenses associated with the target activity of the employee;
- determining, by the expense report generation system, that the data corresponding to target activity of the employee satisfies the expense trigger;
- responsive to determining that the data corresponding to target activity of the employee satisfies the expense trigger: generating, by the expense report generation system, a first expense description for the first set of one or more expenses;
- generating, by the expense report generation system, an expense report comprising the first expense description,
- wherein the method is performed by at least one device comprising a hardware processor.
Type: Application
Filed: Sep 10, 2020
Publication Date: Mar 11, 2021
Applicant: Oracle International Corporation (Redwood Shores, CA)
Inventors: Winston Leonard Wang (San Francisco, CA), Parker Ralph Kuncl (Seattle, WA), Kelly Bailey (San Francisco, CA), Matthew Brigante (Seattle, WA)
Application Number: 17/017,368
